790 results about "Kernel principal component analysis" patented technology

Simultaneous localization and mapping (SLAM) utilizes multiple view feature descriptors to robustly determine location despite appearance changes that would stifle conventional systems. A SLAM algorithm generates a feature descriptor for a scene from different perspectives using kernel principal component analysis (KPCA). When the SLAM module subsequently receives a recognition image after a wide baseline change, it can refer to correspondences from the feature descriptor to continue map building and / or determine location. Appearance variations can result from, for example, a change in illumination, partial occlusion, a change in scale, a change in orientation, change in distance, warping, and the like. After an appearance variation, a structure-from-motion module uses feature descriptors to reorient itself and continue map building using an extended Kalman Filter. Through the use of a database of comprehensive feature descriptors, the SLAM module is also able to refine a position estimation despite appearance variations.

The invention relates to a nonlinear process fault diagnosis method, comprising the steps such as data acquisition, similar analysis, whitening process to the data by utilizing kernel principal component analysis, solving of observation variable z after whitening, independent component exaction by modifying an ICA analysis, and fault detection and diagnosis analysis by utilization of T<2> and SPE statistics and LS-SVM. The invention puts forward the nonlinear dynamic process fault diagnosis technique, combines the advantages of Kernel, ICA and LS-SVM, that is, the exerts the kernel-to-nonlinear express ability, and at the same plays the master ability of ICA to the dynamic characteristic as well as LS - SVM classification ability.

This application discloses a method for distinguishing targets from clutter, comprising the steps inputting data, calculating data statistics from said data and using said data statistics to select target specific feature information to distinguish specific targets from background clutter, generating said target specific feature information from said data statistics, extracting said target specific feature information from said data, using said target specific feature information to distinguish specific targets from background clutter, and outputting target and background clutter information. Classification systems, including hardware and software embodiments, are also disclosed.

Extracting features from signals for use in classification, retrieval, or identification of data represented by those signals uses a “Distortion Discriminant Analysis” (DDA) of a set of training signals to define parameters of a signal feature extractor. The signal feature extractor takes signals having one or more dimensions with a temporal or spatial structure, applies an oriented principal component analysis (OPCA) to limited regions of the signal, aggregates the output of multiple OPCAs that are spatially or temporally adjacent, and applies OPCA to the aggregate. The steps of aggregating adjacent OPCA outputs and applying OPCA to the aggregated values are performed one or more times for extracting low-dimensional noise-robust features from signals, including audio signals, images, video data, or any other time or frequency domain signal. Such extracted features are useful for many tasks, including automatic authentication or identification of particular signals, or particular elements within such signals.

The invention discloses a genetic algorithm-based support vector regression shipping traffic flow prediction method, comprising the following steps: (1) reducing the dimensional number of factors possibly generating influences on shipping traffic flow by a weighted principal component analysis method, and selecting influencing factors with higher cumulative contribution rate; (2) carrying out normalization preprocessing on original vessel traffic flow time series data to generate a data set and then grouping; (3) selecting a kernel function to determine support vector machine (SVM) regression parameters; (4) constructing a support vector regression prediction model optimized by a genetic algorithm; (5) inputting the data set to generate a prediction function; and (6) predicting according to the prediction function generated in step (5), evaluating and analyzing prediction error, and if the error is relatively large, returning to step (2) and regulating the parameters again, predicting once again. The method of the invention has the advantages of higher prediction accuracy and higher stability of prediction accuracy.

The invention discloses a short period load prediction method based on kernel principle component analysis and a random forest. The a short period load prediction method comprises the following steps of: (1) analyzing and selecting data influencing load prediction precision of a day to be predicted in an operational electric power system, and preliminarily constructing training and prediction sample sets; (2) utilizing kernel principle component analysis to carry out dimensionality reduction on training sample data; (3) utilizing a random forest model to train the training sample data after the dimensionality reduction, and obtaining the random forest model after the training; and (4) inputting prediction sample data into the random forest model after the training, and carrying out short period load prediction of the day to be predicted. The short period load prediction method has the advantages that the kernel principle component analysis and the random forest model are combined for carrying out short period load prediction on the electric power system, the prediction precision, efficiency and data rationality are improved.

Principal Component Analysis (PCA) is used to model a process, and clustering techniques are used to group excursions representative of events based on sensor residuals of the PCA model. The PCA model is trained on normal data, and then run on historical data that includes both normal data, and data that contains events. Bad actor data for the events is identified by excursions in Q (residual error) and T2 (unusual variance) statistics from the normal model, resulting in a temporal sequence of bad actor vectors. Clusters of bad actor patterns that resemble one another are formed and then associated with events.

A stereo audio signal is processed to determine primary and ambient components by transforming the signal into vectors corresponding to subband signals, and decomposing the left and right channel vectors into ambient and primary components by matrix and vector operations. Principal component analysis is used to determine a primary component unit vector, and ambience components are determined according to a correlation-based cross-fade or an orthogonal basis derivation.

The invention provides a feature extraction and matching method and device for a digital image based on PCA (principal component analysis), belonging to the technical field of image analysis. The method comprises the following steps of: 1) detecting scale space extreme points; 2) locating the extreme points; 3) distributing directions of the extreme points; 4) reducing dimension of PCA and generating image feature descriptors; and 5) judging similarity measurement and feature matching. The device mainly comprises a numerical value preprocessing module, a feature point extraction module and a feature point matching module. Compared with the existing SIFI (Scale Invariant Feature Transform) feature extraction and matching algorithm, the feature extraction and matching method has higher accuracy and matching speed. The method and device provided by the invention can be directly applied to such machine vision fields as digital image retrieval based on contents, digital video retrieval based on contents, digital image fusion and super-resolution image reconstruction.

The invention relates to a non-linear fault identifying method based on a PCA (principal component analysis) contribution diagram, and includes five steps, namely, collecting data, extracting the base of the feature space, extracting the non-linear principal component, detecting faults and identifying faults. The invention puts forward a new method for extracting the base of the feature space, reduces the linear redundancy after the data are mapped to the feature space by extracting the base and decreases the calculation amount of KPCA when training samples are large in quantity. The invention adopts the contribution diagram to identify faults, calculates out the contribution diagram and the control limit for each variant in the process and determines the responsibility for the occurrenceof faults that each variant should shoulder when each variant is out of control through the relationship between the contribution diagram and the control limit. The identifying method overcomes the trouble in identifying faults due to the fact that the input space and the feature space cannot be converted freely.

A computer implemented method, system and program product for automatic fault classification. A set of abnormal data can be automatically grouped based on sensor contribution to a prediction error. A principal component analysis (PCA) model of normal behavior can then be applied to a set of newly generated data, in response to automatically grouping the set of abnormal data based on the sensor contribution to the prediction error. Data points can then be identified, which are indicative of abnormal behavior. Such an identification step can occur in response to applying the principal component analysis mode of normal behavior to the set of newly generated data in order to cluster and classify the data points in order to automatically classify one or more faults thereof. The data points are automatically clustered, in order to identify a set of similar events, in response to identifying the data points indicative of abnormal behavior.

In a method of analyzing patient physiological data, the data is subjected to principal component analysis and compared to a model physiological data principal component analysis. The comparison is used to identify correlations present in the morphology of the patient physiological data. The present invention further includes determining a confidence interval for the detection of a morphological feature and utilizing this confidence interval for improving the quality of the detection of morphological features of the patient physiological data, including automated morphological feature identification.

The invention discloses a time series data cleaning method based on correlation analysis and principal component analysis, comprising the following steps: finding out hidden correlation between transformer faults and other power data by using a Pearson's correlation coefficient (PCC); reducing the dimension and noise of all relevant time series through principal component analysis (PCA); and inputting part of cleaned data as a training set into a BP neural network (BPNN) for training and learning, and taking the remaining data as a test set to verify a model. Compared with the traditional technology, the accuracy of transformer fault diagnosis is significantly improved, the accuracy of classification is improved, and the operation time is shorter for high-dimensional data.

Principal Component Analysis (PCA) is used to model a process, and clustering techniques are used to group excursions representative of events based on sensor residuals of the PCA model. The PCA model is trained on normal data, and then run on historical data that includes both normal data, and data that contains events. Bad actor data for the events is identified by excursions in Q (residual error) and T2 (unusual variance) statistics from the normal model, resulting in a temporal sequence of bad actor vectors. Clusters of bad actor patterns that resemble one another are formed and then associated with events.

The invention relates to a comprehensive evaluation method of smart power grid construction based on principal component cluster analysis, which is technologically characterized by comprising the steps that at the step 1, a generally approved comprehensive evaluation index system of the smart power grid construction is established or selected; at the step 2, index data is processed by standardization; at the step 3, an index data correlation coefficient matrix is established, an eigenvalue and an eigenvector of the matrix are solved, and a principal component expression is generated; at the step 4, a principal component variance contribution rate and a cumulative variance contribution rate are calculated, and quantity of principal components is determined; at the step 5, a comprehensive principal component evaluation index function is established, and a comprehensive evaluation result of a development and construction level of a smart power grid is given; and at the step 6, a principal component factor load matrix is established, and the cluster analysis is carried out to comprehensive evaluation indexes of the smart power grid. The comprehensive evaluation method of the smart power grid construction based on the principal component cluster analysis provided by the invention combines principal component analysis and the cluster analysis in order to simplify and reconstruct the evaluation index system of the smart power grid construction and provides suggestions for the smart power grid construction is laggard areas.

A database has a plurality of entries and a plurality of attributes common to each entry, where each entry corresponds to an individual. A query is received from a querying entity query and is passed to the database, and an answer is received in response. An amount of noise is generated and added to the answer to result in an obscured answer, and the obscured answer is returned to the querying entity. The noise is normally distributed around zero with a particular variance. The variance R may be determined in accordance with R>8 T log2(T / δ) / ε2, where T is the permitted number of queries T, δ is the utter failure probability, and ε is the largest admissible increase in confidence. Thus, a level of protection of privacy is provided to each individual represented within the database. Example noise generation techniques, systems, and methods may be used for privacy preservation in such areas as k means, principal component analysis, statistical query learning models, and perceptron algorithms.

The invention discloses a method for detecting changes of SAR (synthetic aperture radar) images on the basis of multi-scale product and principal component analysis ( PCA ), mainly solving the problems that the adaptability is poor, the application range is narrow and the change detection results are subject to image misregistration. The method comprises the following specific implementation procedures: firstly, conducting the logarithmic ratio operation on two inputted time phase SAR images to obtain a difference image; carrying out the wavelet transform on the difference image; carrying out the multi-scale product de-noising on the high-frequency information of each decomposition layer; then, combining the de-noised images of each layer and carrying out the PCA transform, wherein, a first PCA image is used as a new difference image; and finally classifying the new difference image by using the minimum error ratio threshold value of the generalized Gaussian model to obtain the final result image of changes. The experiment shows that the invention can enhance the change information, have strong antinoise performance and reduce the influence of image misregistration, thus having high applicability and can be applied to the disaster detection of SAR images.

The invention relates to a soft sensing method for load parameters of a ball mill. The method is that a hardware supporting platform is used to obtain vibration signals, vibration sound signals and current signals of a ball mill cylinder to soft sense ball mill internal parameters (ratio of material to ball, pulp density and filling ratio) characterizing ball mill load. The method comprises the following steps that: the vibration, the vibration sound, the current data and the time-domain filtering of the ball mill cylinder are acquired, time frequency conversion is conducted to the vibration and the vibration sound data, kernel principal component analysis based nonlinear features of the sub band of the vibration and the vibration sound data in frequency domain are extracted, nonlinear features of the time domain current data are extracted, feature selection is conducted to the fused nonlinear feature data and a soft sensing model based on a least squares support vector machine is established. The soft sensing method of the invention has the advantages that the sensitivity is high, the sensed results are accurate, the practical value and the popularization prospect are very good, and the realization of the stability control, the optimization control, the energy saving and the consumption reduction of the grinding production process is facilitated.

The invention discloses a wind turbine gear box fault recognition method. The method comprises the following steps: historical wind turbine gear box operation data in a certain time range are acquired; autocorrelation analysis is adopted for carrying out wavelet de-noising processing on the historical data; through fast Fourier transform, time domain and frequency domain characteristic parameters in the historical data after de-noising are extracted; a kernel principal component analysis method is adopted to carry out dimensionality reduction on the characteristic parameters, and several nonlinear principal elements with the maximum variance cumulative contribution rate are extracted; the nonlinear principal elements extracted by the historical gear box normal operation data are used for building a normal model, a support vector machine is used for training to guide the nonlinear principal elements extracted by later gear box operation historical data to the model after training, and thus, the gear box fault is recognized. The vibration signal processing ability is improved, and an important role is played in gear box fault recognition.

Principal Component Analysis (PCA) is used to model a process, and clustering techniques are used to group excursions representative of events based on sensor residuals of the PCA model. The PCA model is trained on normal data, and then run on historical data that includes both normal data, and data that contains events. Bad actor data for the events is identified by excursions in Q (residual error) and T2 (unusual variance) statistics from the normal model, resulting in a temporal sequence of bad actor vectors. Clusters of bad actor patterns that resemble one another are formed and then associated with events.

The invention discloses a vehicle driving condition establishment method combining principal component analysis with fuzzy c-mean clustering. The method comprises the steps of extracting satellite positioning data of each road traffic condition in a vehicle dynamic monitoring system of a road transport enterprise, and calculating and dividing the data into small sections of micro-strokes; and performing calculation of characteristic parameters such as an average speed, an idle time proportion and the like for each micro-stroke, obtaining a matrix of a sample quantity (row) X the characteristic parameters (column), adopting mean normalization and principal component analysis for matrix data, selecting preorder principal components which meet the conditions that the cumulative contribution rate of characteristic values of the principal components is greater than 85% and the principal components can comprehensively reflect all the characteristic parameters, performing fuzzy c-mean clustering analysis on scores of the principal components, and clustering the micro-strokes into different groups, namely, screening sub-conditions. An initial synthesis condition is smoothed by adopting a filter with a double-weighted smoothing kernel function. According to the method, existing satellite positioning data is fully utilized; compliance testing is easily carried out on a dynamometer; relatively high universality is achieved; and the research cost of vehicle driving condition establishment is reduced.

The invention discloses a face recognition method based on PCA image reconstruction and LDA and belongs to the technical field of computer image processing and pattern recognition. The face recognition method is based on a principal component analysis algorithm; an intra-class covariance matrix serves as a generation matrix for acquiring face feature subspaces of individuals; then an image to be recognized maps the feature subspaces to extract features; the image is reconstructed according to feature values; a residual image is subjected to the linear discriminant analysis; and finally, face recognition is realized by a minimum distance classification recognition algorithm. Compared with the prior feature subspace method, the face recognition method can better extract the face features of different people, and the face recognition rate is increased effectively. In addition, when a face database is required to be expanded, only the newly added faces are required for feature face training; not all the face feature subspaces are retrained; and the face recognition method also has good extendibility.

The invention discloses a polarized SAR (synthetic aperture radar) image classification method based on a depth PCA (principal component analysis) network and an SVM (support vector machine) classifier. The polarized SAR image classification method includes filtering a polarized SAR image, extracting a shape feature parameter, a scattering feature parameter, a polarization feature parameter and independent elements of a covariance matrix C, and combing and normalizing into new high-dimensional features serving as data to be processed in a next step; according to actual ground feature flags, randomly selecting 10% of data with flags from each type to serve as training samples; whitening the training samples to serve as input to train a first layer of the network, taking a result as input of a second layer to train the second layer of the network, and performing binaryzation and histogram statistics on an output result; taking output of the depth PCA network as a finally learned feature training SVM classifier; whitening test samples, and inputting the test samples into a trained network framework to predict and calculate accuracy; coloring and displaying a classified image and outputting a final result.

The invention discloses a gastrointestinal tumor microscopic hyper-spectral image processing method based on a convolutional neural network, comprising the following steps: reducing and de-noising the spectral dimension of an acquired gastrointestinal tissue hyper-spectral training image; constructing a convolutional neural network structure; and inputting obtained hyper-spectral data principal components (namely, a plurality of 2D gray images, which are equivalent to a plurality of feature maps of an input layer) as input images into the constructed convolutional neural network structure using a batch processing method, and by taking a cross entropy function as a loss function and using an error back propagation algorithm, training the parameters in the convolutional neural network and the parameters of a logistic regression layer according to the average loss function in a training batch until the network converges. According to the invention, the dimension of a hyper-spectral image is reduced using a principal component analysis method, enough spectral information and spatial texture information are retained, the complexity of the algorithm is reduced greatly, and the efficiency of the algorithm is improved.

The invention relates to a rice disease recognition method based on principal component analysis and a neural network. The method comprises the steps that rice disease image data are acquired and image preprocessing is performed; visual saliency detection is performed, and rice disease images of ideal disease spot outlines are searched from salient map sequences; features are extracted from the rice disease images from the aspects of color, shape and texture, and difference analysis and principal component analysis are performed so that different feature combinations are found; and construction of a machine learning model is performed on different feature combinations and a prediction result is fed back to a client side. The invention also discloses a rice disease recognition system based on principal component analysis and the neural network. Image information is acquired and the images are transmitted to a server side through the network. Preprocessing and disease spot detection are performed on the acquired tissue culturing images through the server side, and management personnel are prompted through a mobile phone short message and a signal lamp and a PC side according to the detection result.

The invention discloses a multilevel inverter fault diagnosis strategy based on a principal component analysis and multi-classification related vector machines (PCA-mRVM). The multilevel inverter fault diagnosis strategy includes: subjecting primary samples to dimensionality reduction through principal component analysis, and extracting multiple principal components with fault features so as to form training samples; subjecting the training samples to fault diagnosis through the multi-classification related vector machine, outputting probabilities of fault classifications, and taking the fault classifications with the maximum probabilities as diagnosis results. The multilevel inverter fault diagnosis strategy has the advantages in the fault diagnosis with larger sample space and more classifications, is high in model sparseness, low in computation complexity and the like; most importantly, the probabilities of classification members can be outputted through the mRVM, probability and statistic significance is achieved, and uncertain problems can be conveniently analyzed.

The invention relates to an online updating method of a principal component analysis monitoring model. The method comprises the following steps that: 1) A model online updating system comprising data acquisition equipment and a monitoring computer is arranged in industry field; 2) A traditional principal component analysis (PCA) modeling module uses historical data to establish a PCA initial monitoring model; 3) After the monitoring begins, a mean value variance updating module calculates a mean value and a standard deviation sigma' of a new model according to real-time process data and the current PCA model; 4) A projection point calculation module calculates a residual vector of a new sample and transmits to a residual determination module; 5) The residual determination module determines an updating method of a projection direction according to a size of a residual vector die; if the residual is large, a principal component space adjusting module is called; if the residual value is small, a principal component direction fine adjusting module is called; finally a load vector P' nk and a characteristic value matrix lambda' kk of the new model is obtained; 6) A control limit updating module carries out control limit and updating on statistical magnitude of the model; the system finally outputs the new model omega' which is used for online monitoring and fault diagnosis during an industrial process.

The invention discloses a face recognition method based on reference features. The method comprises the following steps that: scale invariant features and local binary pattern features of a face image to be recognized are extracted; a principal component analysis method is utilized for dimensionality reduction to obtain the image features of the face image to be recognized; the similarity of the image features to a cluster center is calculated by utilizing the obtained image features to obtain the reference features of the face image to be recognized; and the similarity of the reference features of the face image to be recognized and the reference features of training data concentration is calculated to obtain an analysis result. The reference features of the face image provided by the invention comprise texture information and structure information of the face image, so that the method provided by the invention can more comprehensively represent the face compared with the method in the prior art, which only represents the texture information or the structure information of the face. The process of feature extraction is simple and easy to realize; the recognition result is highly precise; high recognition rate of different facial gestures of the same person is realized.

The invention discloses a two-stage audio search method, which comprises the following steps: 1) extracting the audio features of an audio file in a database; 2) carrying out text-based search for the audio file in the database; 3) composing a training set based on the searched audio file, and searching for a characteristic set which is most reliable for sorting by adopting a principal component analysis method; 4) based on the training set, training the weak classifier composed of the characteristic set so as to compose a strong classifier; and 5) searching by the strong classifier obtained in step 4). The two-stage audio search method is suitable for any content-based recommendation system of an audio set, the searched part of which is remarked, and is also suitable for any non-text objects, such as images and videos.